Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

Abstract

Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.

Full text

Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
Author(s): Fred D. Davis
Source:
MIS Quarterly,
Vol. 13, No. 3 (Sep., 1989), pp. 319-340
Published by: Management Information Systems Research Center, University of Minnesota
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IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
IT
Usefulness
and Ease of Use
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
Perceived
Usefulness,
Perceived
Ease
of
Use,
and
User
Acceptance
of
Information
Technology
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
By: Fred D. Davis
Computer and Information Systems
Graduate School of Business
Administration
University of Michigan
Ann Arbor, Michigan 48109
Abstract
Valid
measurement scales for predicting user
acceptance of computers are in short supply.
Most subjective measures used in practice are
unvalidated, and their relationship to system
usage is unknown. The present research de-
velops and validates new scales for two spe-
cific variables, perceived usefulness and per-
ceived ease of use, which are hypothesized to
be fundamental determinants
of user accep-
tance. Definitions for these two variables were
used to develop scale items that were pretested
for
content validity
and then tested for
reliability
and construct validity
in two studies involving
a total of 152 users and four application pro-
grams. The measures were refined and stream-
lined, resulting
in two six-item scales with reli-
abilities of .98 for usefulness and .94 for ease
of use. The scales exhibited high convergent,
discriminant,
and factorial
validity.
Perceived use-
fulness was significantly
correlated
with both self-
reported current usage (r=.63, Study 1) and
self-predicted
future
usage (r= .85, Study 2). Per-
ceived ease of use was also significantly
corre-
lated with
current
usage (r=.45, Study 1) and
future
usage (r=.59, Study 2). In both studies,
usefulness had a significantly
greater correla-
tion with
usage behavior
than did ease of use.
Regression analyses suggest that perceived
ease of use may actually be a causal antece-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
dent to perceived usefulness, as opposed to
a parallel,
direct determinant
of system usage.
Implications
are drawn for future research on
user acceptance.
Keywords: User acceptance, end user
computing,
user measurement
ACM
Categories: H.1.2, K.6.1, K.6.2, K.6.3
Introduction
Information
technology
offers the potential
for
sub-
stantially improving
white collar performance
(Curley,
1984; Edelman, 1981; Sharda, et al.,
1988). But performance gains are often ob-
structed by users' unwillingness
to accept and
use available systems (Bowen, 1986; Young,
1984). Because of the persistence and impor-
tance of this problem, explaining user accep-
tance has been a long-standing
issue in MIS
research (Swanson, 1974; Lucas, 1975; Schultz
and Slevin, 1975; Robey, 1979; Ginzberg,
1981;
Swanson, 1987). Although
numerous
individual,
organizational,
and technological
variables
have
been investigated
(Benbasat and Dexter, 1986;
Franz and Robey, 1986; Markus and Bjorn-
Anderson, 1987; Robey and Farrow,
1982), re-
search has been constrained by the shortage
of high-quality
measures for key determinants
of user acceptance. Past research indicates
that
many measures do not correlate highly with
system use (DeSanctis, 1983; Ginzberg,
1981;
Schewe, 1976; Srinivasan,
1985), and the size
of the usage correlation varies greatly
from
one
study to the next depending on the particular
measures used (Baroudi,
et al., 1986; Barki and
Huff,
1985; Robey, 1979; Swanson, 1982, 1987).
The development
of improved
measures for
key
theoretical
constructs is a research priority
for
the information
systems field.
Aside from
their theoretical
value, better
meas-
ures for predicting
and explaining system use
would have great practical
value, both for ven-
dors who would like to assess user demand for
new design ideas, and for information
systems
managers within
user organizations
who would
like
to evaluate these vendor offerings.
Unvalidated measures are routinely
used in prac-
tice today throughout
the entire spectrum of
design, selection, implementation
and evaluation
activities. For
example: designers within
vendor
organizations
such as IBM
(Gould,
et al., 1983),
Xerox
(Brewley,
et al., 1983), and Digital Equip-
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
MIS
Quarterly/September
1989 319
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IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
IT
Usefulness
and Ease
of Use
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
ment Corporation
(Good, et al., 1986) measure
user perceptions to guide the development of
new information
technologies and products;
in-
dustry publications often report user surveys
(e.g., Greenberg,
1984; Rushinek and Rushinek,
1986); several methodologies for software se-
lection call for subjective user inputs (e.g.,
Goslar, 1986; Klein
and Beck, 1987); and con-
temporary
design principles
emphasize meas-
uring
user reactions
throughout
the entire
design
process (Anderson
and Olson 1985; Gould
and
Lewis,
1985; Johansen and Baker, 1984; Mantei
and Teorey, 1988; Norman, 1983; Shneiderman,
1987). Despite the widespread use of subjec-
tive measures in practice,
little
attention
is paid
to the quality
of the measures used or how well
they correlate with usage behavior. Given the
low usage correlations often observed in re-
search studies, those who base important
busi-
ness decisions on unvalidated
measures may
be getting
misinformed
about
a system's accept-
ability
to users.
The purpose of this research is to pursue better
measures for
predicting
and explaining
use. The
investigation focuses on two theoretical con-
structs, perceived usefulness and perceived
ease of use, which are theorized to be funda-
mental determinants of system use. Definitions
for
these constructs
are formulated and the theo-
retical
rationale for their hypothesized
influence
on system use is reviewed.
New,
multi-item
meas-
urement
scales for
perceived
usefulness and per-
ceived ease of use are developed, pretested,
and
then
validated in two
separate
empirical
stud-
ies. Correlation and regression analyses exam-
ine the empirical
relationship
between the new
measures and self-reported
indicants of system
use. The discussion concludes by drawing
im-
plications
for future research.
Perceived Usefulness and
Perceived Ease of Use
What
causes people to accept or reject
informa-
tion
technology? Among
the many
variables
that
may
influence
system use, previous
research
sug-
gests two determinants
that are especially im-
portant.
First,
people tend to use or not use an
application
to the extent they believe it will
help
them perform
their
job better. We refer to this
first
variable as perceived usefulness. Second,
even if potential
users believe that a given ap-
plication
is useful, they may, at the same time,
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
believe that the systems is too hard to use and
that
the performance
benefits of usage are out-
weighed by the effort of using the application.
That
is, in addition
to usefulness, usage is theo-
rized
to be influenced
by perceived ease of use.
Perceived usefulness is defined here as "the
degree to which a person believes that using
a particular
system would enhance his or her
job performance."
This follows from
the defini-
tion of the word useful: "capable
of being used
advantageously."
Within
an organizational
con-
text, people are generally reinforced
for good
performance
by raises, promotions, bonuses,
and other rewards
(Pfeffer,
1982; Schein, 1980;
Vroom,
1964). A system high in perceived use-
fulness, in turn,
is one for which a user believes
in the existence of a positive use-performance
relationship.
Perceived ease of use, in contrast,
refers
to "the
degree to which a person believes that using
a particular
system would
be free of effort."
This
follows from the definition
of "ease": "freedom
from difficulty
or great effort."
Effort is a finite
resource
that a person may allocate to the vari-
ous activities
for which he or she is responsible
(Radner and Rothschild,
1975). All else being
equal, we claim, an application perceived
to be
easier to use than another is more likely
to be
accepted by users.
Theoretical Foundations
The theoretical
importance
of perceived useful-
ness and perceived
ease of use as determinants
of user behavior is indicated
by several diverse
lines of research. The impact
of perceived use-
fulness on system utilization
was suggested by
the work
of Schultz
and Slevin
(1975) and Robey
(1979). Schultz
and Slevin (1975) conducted
an
exploratory
factor analysis of 67 questionnaire
items, which yielded seven dimensions. Of
these, the "performance"
dimension,
interpreted
by the authors as the perceived "effect of the
model on the manager's
job performance,"
was
most highly
correlated with
self-predicted
use of
a decision model (r=.61). Using
the Schultz
and
Slevin
questionnaire,
Robey (1979) finds
the per-
formance
dimension
to be most correlated
with
two objective
measures of system usage (r=.79
and .76). Building
on Vertinsky,
et al.'s (1975)
expectancy model, Robey (1979) theorizes
that:
"A system that does not help people perform
their
jobs is not likely
to be received favorably
320 MIS
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1989
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320 MIS
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1989
320 MIS
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320 MIS
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320 MIS
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1989
320 MIS
Quarterly/September
1989
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IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
in spite of careful implementation
efforts" (p.
537). Although
the perceived use-performance
contingency, as presented in Robey's (1979)
model, parallels
our definition of perceived use-
fulness, the use of Schultz and Slevin's (1975)
performance
factor to operationalize perform-
ance expectancies
is problematic
for
several rea-
sons: the instrument
is empirically
derived via
exploratory
factor
analysis;
a somewhat low ratio
of sample size to items is used (2:1); four of
thirteen items have loadings below .5, and sev-
eral of the items clearly fall outside the defini-
tion of expected performance improvements
(e.g., "My job will be more satisfying,"
"Others
will
be more aware of what I am doing,"
etc.).
An alternative
expectancy-theoretic
model, de-
rived from Vroom (1964), was introduced and
tested by DeSanctis (1983). The use-perform-
ance expectancy was not analyzed separately
from performance-reward
instrumentalities and
reward valences. Instead,
a matrix-oriented
meas-
urement
procedure
was used to produce
an over-
all index of "motivational
force"
that combined
these three constructs. "Force" had small but
significant correlations with usage of a DSS
within
a business simulation
experiment
(corre-
lations
ranged
from .04 to .26). The contrast be-
tween DeSanctis's correlations
and the ones ob-
served by Robey underscore
the importance
of
measurement in predicting
and explaining
use.
Self-efficacy
theory
The importance
of perceived
ease of use is sup-
ported
by Bandura's
(1982) extensive research
on self-efficacy,
defined as "judgments
of how
well one can execute courses of action required
to deal with
prospective
situations"
(p. 122). Self-
efficacy is similar
to perceived ease of use as
defined above. Self-efficacy
beliefs
are theorized
to function
as proximal
determinants
of behav-
ior. Bandura's
theory distinguishes
self-efficacy
judgments from outcome judgments, the latter
being concerned with the extent to which a be-
havior,
once successfully executed, is believed
to be linked to valued
outcomes. Bandura's
"out-
come judgment"
variable
is similar
to perceived
usefulness. Bandura argues that self-efficacy
and outcome beliefs have differing
antecedents
and that, "In
any given instance, behavior
would
be best predicted by considering both self-
efficacy and outcome beliefs" (p. 140).
Hill,
et al. (1987) find that both self-efficacy
and
outcome beliefs exert an influence on decisions
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
to learn a computer
language. The self efficacy
paradigm
does not offer
a general measure ap-
plicable to our purposes since efficacy beliefs
are theorized to be situationally-specific,
with
measures tailored to the domain under study
(Bandura,
1982). Self efficacy research does,
however,
provide
one of several theoretical
per-
pectives suggesting that perceived ease of use
and perceived usefulness function
as basic de-
terminants of user behavior.
Cost-benefit
paradigm
The cost-benefit
paradigm
from
behavioral deci-
sion theory
(Beach and Mitchell, 1978; Johnson
and Payne, 1985; Payne, 1982) is also relevant
to perceived usefulness and ease of use. This
research explains people's choice among vari-
ous decision-making
strategies (such as linear
compensatory,
conjunctive,
disjunctive
and elmi-
nation-by-aspects)
in terms of a cognitive
trade-
off
between the effort
required
to employ
the strat-
egy and the quality
(accuracy)
of the resulting
decision. This approach has been effective for
explaining
why decision
makers alter
their choice
strategies in response to changes in task com-
plexity.
Although
the cost-benefit
approach
has
mainly concerned itself with unaided decision
making, recent work has begun to apply the
same form of analysis to the effectiveness of
information
display formats (Jarvenpaa, 1989;
Kleinmuntz and Schkade, 1988).
Cost-benefit research has primarily
used objec-
tive measures of accuracy
and effort
in research
studies, downplaying
the distinction
between ob-
jective and subjective accuracy and effort. In-
creased emphasis
on subjective
constructs is war-
ranted, however, since (1) a decision maker's
choice of strategy is theorized
to be based on
subjective
as opposed to objective
accuracy
and
effort
(Beach and Mitchell,
1978), and (2) other
research
suggests that subjective
measures are
often in disagreement
with their
ojbective
coun-
terparts (Abelson
and Levi, 1985; Adelbratt
and
Montgomery,
1980; Wright,
1975). Introducing
measures of the decision maker's own perceived
costs and benefits, independent
of the decision
actually made, has been suggested as a way
of mitigating
criticisms
that the cost/benefit frame-
work is tautological
(Abelson and Levi, 1985).
The distinction
made herein between perceived
usefulness and perceived
ease of use is similar
to the distinction
between subjective decision-
making performance
and effort.
MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321 MIS Quarterly/September 1989 321
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
Adoption
of innovations
Research on the adoption of innovations
also
suggests a prominent
role for perceived ease
of use. In
their
meta-analysis
of the relationship
between the characteristics of an innovation
and
its adoption,
Tornatzky
and Klein
(1982) find
that
compatibility,
relative
advantage, and complex-
ity have the most consistent significant
relation-
ships across a broad
range of innovation
types.
Complexity,
defined by Rogers and Shoemaker
(1971) as "the degree to which an innovation
is perceived as relatively
difficult
to understand
and use" (p. 154), parallels perceived ease of
use quite
closely. As Tornatzky
and Klein
(1982)
point
out, however,
compatibility
and relative ad-
vantage have both been dealt with so broadly
and inconsistently
in the literature
as to be diffi-
cult to interpret.
Evaluation of information
reports
Past research within
MIS on the evaluation
of
information
reports echoes the distinction
be-
tween usefulness and ease of use made herein.
Larcker
and Lessig (1980) factor analyzed six
items used to rate four information
reports.
Three
items load on each of two distinct
factors: (1)
perceived importance,
which Larcker
and Lessig
define as "the quality
that causes a particular
information set to acquire relevance to a deci-
sion maker,"
and the extent to which the infor-
mation
elements are "a necessary input
for task
accomplishment," and (2) perceived usable-
ness, which is defined as the degree to which
"the information format is unambiguous,
clear
or readable"
(p. 123). These two dimensions are
similar to perceived usefulness and perceived
ease of use as defined above, repsectively,
al-
though Larcker and Lessig refer to the two di-
mensions
collectively
as "perceived
usefulness."
Reliabilities
for the two dimensions fall in the
range of .64-.77, short of the .80 minimal level
recommended for basic research. Correlations
with actual use of information
reports
were not
addressed in their study.
Channel
disposition
model
Swanson (1982, 1987) introduced and tested a
model of "channel
disposition"
for
explaining
the
choice and use of information
reports.
The con-
cept of channel disposition
is defined as having
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
two components: attributed
information
quality
and attributed access quality.
Potential
users are
hypothesized
to select and use information
re-
ports based on an implicit
psychological
trade-
off between information
quality
and associated
costs of access. Swanson (1987) performed
an
exploratory
factor analysis in order to measure
information
quality
and access quality.
A five-
factor
solution
was obtained,
with
one factor
cor-
responding to information
quality (Factor #3,
"value"),
and one to access quality
(Factor
#2,
"accessibility"). Inspecting
the items that
load
on
these factors suggests a close correspondence
to perceived usefulness and ease of use. Items
such as "important,"
"relevant,"
"useful,"
and
"valuable"
load
strongly
on the value dimension.
Thus, value parallels
perceived usefulness. The
fact that relevance and usefulness load on the
same factor agrees with information
scientists,
who emphasize the conceptual similarity
be-
tween the usefulness and relevance notions
(Saracevic,
1975). Several of Swanson's "acces-
sibility"
items, such as "convenient,"
"controlla-
ble," "easy,"
and "unburdensome,"
correspond
to perceived ease of use as defined above. Al-
though
the study
was more
exploratory
than
con-
firmatory,
with no attempts at construct
valida-
tion,
it
does agree with
the conceptual
distinction
between usefulness and ease of use. Self-
reported
information
channel use correlated
.20
with the value dimension and .13 with the ac-
cessibility
dimension.
Non-MIS
studies
Outside
the MIS
domain, a marketing study by
Hauser
and Simmie
(1981) concerning
user per-
ceptions of alternative
communication
technolo-
gies similarly
derived two underlying
dimensions:
ease of use and effectiveness, the latter
being
similar
to the perceived
usefulness construct
de-
fined
above. Both
ease of use and effectiveness
were influential
in the formation of user prefer-
ences regarding
a set of alternative
communi-
cation
technologies. The human-computer
inter-
action (HCI) research community
has heavily
emphasized ease of use in design (Branscomb
and Thomas, 1984; Card, et al., 1983; Gould
and Lewis, 1985). For the most part, however,
these studies have focused on objective
meas-
ures of ease of use, such as task completion
time and error rates. In many vendor
organiza-
tions, usability testing has become a standard
phase in the product
development cycle, with
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
322 MIS
Quarterly/September
1989
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IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
IT
Usefulness and
Ease
of Use
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
large
investments
in
test facilities
and instrumen-
tation.
Although
objective
ease of use is clearly
relevant to user performance given the system
is used, subjective
ease of use is more relevant
to the users' decision whether or not to use the
system and may not agree with the objective
measures (Carroll
and Thomas, 1988).
Convergence
of findings
There is a striking
convergence among the wide
range of theoretical
perspectives and research
studies discussed above. Although Hill, et al.
(1987) examined learning
a computer language,
Larcker
and Lessig (1980) and Swanson (1982,
1987) dealt with evaluating
information
reports,
and Hauser and Simmie (1981) studied com-
munication
technologies,
all are supportive
of the
conceptual
and empirical
distinction
between use-
fulness and ease of use. The accumulated
body
of knowledge regarding
self-efficacy, contingent
decision behavior and adoption of innovations
provides
theoretical
support
for perceived use-
fulness and ease of use as key determinants
of behavior.
From multiple
disciplinary vantage points, per-
ceived usefulness and perceived ease of use
are indicated as fundamental
and distinct con-
structs
that are influential
in decisions to use in-
formation
technology.
Although
certainly
not the
only variables of interest in explaining
user be-
havior
(for other variables, see Cheney, et al.,
1986; Davis, et al., 1989; Swanson, 1988), they
do appear likely
to play a central
role. Improved
measures are needed to gain further
insight
into
the nature of perceived usefulness and per-
ceived ease of use, and their roles as determi-
nants of computer
use.
Scale Development and
Pretest
A step-by-step process was used to develop
new multi-item scales having
high reliability
and
validity.
The conceptual
definitions
of perceived
usefulness and perceived ease of use, stated
above, were used to generate 14 candidate
items
for
each construct
from
past literature.
Pre-
test interviews
were then conducted to assess
the semantic content of the items. Those items
that best fit the definitions
of the constructs
were
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
retained,
yielding 10 items for each construct.
Next, a field study (Study 1) of 112 users con-
cerning two different
interactive
computer sys-
tems was conducted
in order
to assess the reli-
ability and construct validity of the resulting
scales. The scales were further refined and
streamlined to six items per construct. A lab
study (Study
2) involving
40 participants
and two
graphics systems was then conducted. Data
from
the two studies were then used to assess
the relationship
between usefulness, ease of
use, and self-reported
usage.
Psychometricians
emphasize that
the validity
of
a measurement
scale is built
in from the outset.
As Nunnally
(1978) points
out, "Rather than test
the validity
of measures after they have been
constructed,
one should ensure the validity by
the plan and procedures for construction"
(p.
258). Careful selection of the initial scale items
helps to assure the scales will
possess "content
validity,"
defined as "the degree to which the
score or scale being used represents the con-
cept about which generalizations are to be
made" (Bohrnstedt,
1970, p. 91). In discussing
content validity, psychometricians
often appeal
to the "domain
sampling model," (Bohrnstedt,
1970; Nunnally,
1978) which assumes there is
a domain of content
corresponding
to each vari-
able one is interested in measuring.
Candidate
items representative
of the domain of content
should be selected. Researchers are advised to
begin by formulating
conceptual definitions of
what is to be measured and preparing
items to
fit the construct definitions
(Anastasi, 1986).
Following
these recommendations, candidate
items for perceived usefulness and perceived
ease of use were generated based on their con-
ceptual definitions,
stated above, and then pre-
tested in order to select those items that best
fit the content domains. The Spearman-Brown
Prophecy formula was used to choose the
number of items
to generate for each scale. This
formula estimates the number
of items needed
to achieve a given reliability based on the
number of items and reliability
of comparable
existing
scales. Extrapolating
from
past studies,
the formula
suggests that 10 items would be
needed for each perceptual
variable
to achieve
reliability
of at least .80 (Davis, 1986). Adding
four
additional
items for each construct
to allow
for item elimination,
it was decided to generate
14 items for each construct.
The initial item pools for perceived usefulness
and perceived ease of use are given in Tables
MIS
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1989 323
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1989 323
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1989 323
MIS
Quarterly/September
1989 323
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1989 323
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1989 323
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1989 323
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Quarterly/September
1989 323
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IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use IT
Usefulness
and
Ease
of Use
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
1 and 2, respectively. In preparing
candidate
items,
37 published
research
papers
dealing
with
user reactions to interactive
systems were re-
viewed in other to identify
various facets of the
constructs that should be measured (Davis,
1986). The items are worded
in reference to "the
electronic
mail
system," which is one of the two
test applications
investigated
in Study
1, reported
below. The items within each pool tend to have
a lot of overlap in their meaning, which is con-
sistent with the fact that they are intended as
measures of the same underlying construct.
Though
different
individuals
may
attribute
slightly
different
meaning to particular
item statements,
the goal of the multi-item
approach
is to reduce
any extranneous effects of individual
items, al-
lowing idiosyncrasies to be cancelled out by
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
other items in order to yield a more pure indi-
cant of the conceptual variable.
Pretest interviews were performed
to further en-
hance content validity by assessing the corre-
spondence
between candidate
items and the defi-
nitions of the variables they are intended to
measure. Items that don't
represent
a construct's
content
very
well can be screened out by asking
individuals to rank the degree to which each item
matches the variable's
definition,
and eliminat-
ing items receiving
low rankings.
In eliminating
items, we want to make sure not to reduce the
representativeness
of the item pools. Our item
pools may have excess coverage of some areas
of meaning
(or
substrata;
see Bohrnstedt,
1970)
within
the content domain and not enough of
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
Table 1. Initial Scale Items for Perceived Usefulness
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
1. My
job would be difficult to perform
without
electronic
mail.
2. Using electronic
mail
gives me greater
control over my work.
3. Using electronic mail
improves my job performance.
4. The electronic mail
system addresses my job-related
needs.
5. Using electronic
mail saves me time.
6. Electronic mail
enables me to accomplish
tasks more quickly.
7. Electronic
mail
supports
critical
aspects of my job.
8. Using electronic
mail allows me to accomplish
more
work than would
otherwise
be
possible.
9. Using electronic mail reduces the time I spend on unproductive
activities.
10. Using electronic
mail enhances my effectiveness on the job.
11. Using electronic
mail
improves
the quality
of the work
I do.
12. Using electronic mail increases my productivity.
13. Using electronic
mail makes it easier to do my job.
14. Overall,
I find the electronic
mail
system useful in my job.
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
Table 2. Initial Scale Items for Perceived Ease of Use
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
1. I often become confused when I use the electronic mail
system.
2. I make errors
frequently
when using electronic mail.
3. Interacting
with
the electronic mail
system is often frustrating.
4. I need to consult the user manual often when using electronic
mail.
5. Interacting
with the electronic
mail
system requires
a lot of my mental effort.
6. I find
it easy to recover from
errors
encountered while using electronic
mail.
7. The electronic
mail
system is rigid
and inflexible to interact
with.
8. I find it easy to get the electronic mail
system to do what I want it to do.
9. The electronic
mail
system often behaves in unexpected ways.
10. I find it cumbersome,to
use the electronic mail
system.
11. My
interaction
with
the electronic mail
system is easy for me to understand.
12. It
is easy for me to remember
how to perform
tasks using the electronic mail
system.
13. The electronic
mail
system provides
helpful
guidance in performing
tasks.
14. Overall,
I find the electronic
mail
system easy to use.
324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989 324 MIS Quarterly/September
1989
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
others. By asking individuals
to rate the similar-
ity of items to one another, we can perform
a
cluster
analysis to determine
the structure of the
substrata,
remove
items
where excess coverage
is suggested, and add items where inadequate
coverage is indicated.
Pretest participants
consisted of a sample of 15
experienced computer users from the Sloan
School of Management,
MIT,
including
five sec-
retaries,
five graduate students and five mem-
bers of the professional
staff. In
face-to-face in-
terviews,
participants
were asked to perform
two
tasks, prioritization
and categorization, which
were done separately for usefulness and ease
of use. For prioritization,
they were first given
a card
containing
the definition
of the target
con-
struct
and asked to read
it.
Next,
they were given
13 index cards each having one of the items
for
that construct
written on it. The 14th or "over-
all" item for each construct
was omitted since
its wording
was almost identical
to the label on
the definition
card (see Tables 1 and 2). Partici-
pants were asked to rank
the 13 cards accord-
ing to how well the meaning of each statement
matched the given definition
of ease of use or
usefulness.
For the categorization
task, participants
were
asked to put
the 13 cards into
three to five cate-
gories so that the statements within
a category
were most similar
in meaning
to each other and
dissimilar
in meaning from those in other cate-
gories. This was an adaptation
of the "own cate-
gories" procedure
of Sherif and Sherif (1967).
Categorization
provides
a simple
indicant
of simi-
larity
that requires
less time and effort
to obtain
than other similarity
measurement procedures
such as paid comparisons. The similarity
data
was cluster analyzed by assigning to the same
cluster items that
seven or more
subjects placed
in the same category. The clusters are consid-
ered to be a reflection
of the domain substrata
for each construct
and serve as a basis of as-
sessing coverage, or representativeness,
of the
item pools.
The resulting
rank and cluster
data are summa-
rized in Tables 3 (usefulness) and 4 (ease of
use). For
perceived
usefulness, notice
that
items
fall into
three main clusters. The first
cluster re-
lates to job effectiveness, the second to produc-
tivity
and time savings, and the third
to the im-
portance of the system to one's job. If we
eliminate the lowest-ranked
items (items 1, 4,
5 and 9), we see that the three major
clusters
each have at least two items. Item 2, "control
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
over work"
was retained
since, although
it was
ranked
fairly
low, it fell in the top 9 and may
tap an important
aspect of usefulness.
Looking
now at perceived ease of use (Table
4), we again find three main clusters. The first
relates to physical effort,
while the second re-
lates to mental
effort.
Selecting the six highest-
priority
items and eliminating
the seventh pro-
vides good coverage of these two clusters. Item
11 ("understandable")
was reworded to read
"clear and understandable"
in an effort
to pick
up some of the content of item 1 ("confusing"),
which has been eliminated.
The third cluster is
somewhat more difficult
to interpret
but
appears
to be tapping
perceptions
of how easy a system
is to learn.
Remembering
how to perform
tasks,
using the manual, and relying
on system guid-
ance are all
phenomena
associated
with the proc-
ess of learning
to use a new system (Nickerson,
1981; Roberts
and Moran,
1983). Further review
of the literature
suggests that ease of use and
ease of learning are strongly related. Roberts
and Moran
(1983) find a correlation
of .79 be-
tween objective measures of ease of use and
ease of learning.
Whiteside, et al. (1985) find
that ease of use and ease of learning are
strongly
related
and conclude
that
they are con-
gruent. Studies of how people learn new sys-
tems suggest that learning
and using are not
separate, disjoint activities, but instead that
people are motivated
to begin performing
actual
work
directly
and try
to "learn
by doing"
as op-
posed to going through
user manuals or online
tutorials
(Carroll
and Carrithers, 1984; Carroll,
et al., 1985; Carroll
and McKendree,
1987).
In this study, therefore, ease of learning
is re-
garded as one substratum
of the ease of use
construct, as opposed to a distinct construct.
Since items 4 and 13 provide
a rather indirect
assessment of ease of learning,
they were re-
placed with two items that more directly get at
ease of learning:
"Learning
to operate the elec-
tronic mail system is easy for me," and "I
find
it takes a lot of effort
to become skillful
at using
electronic mail." Items 6, 9 and 2 were elimi-
nated because they did not cluster with other
items, and they received low priority
rankings,
which suggests that they do not fit well within
the content domain for ease of use. Together
with
the "overall"
items for each construct,
this
procedure
yielded a 10-item scale for
each con-
struct
to be empirically
tested for reliability
and
construct
validity.
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
MIS
Quarterly/September
1989 325
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IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
IT
Usefulness and
Ease of Use
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Table 3. Pretest Results: Perceived Usefulness
Old New
Item # Item Rank Item # Cluster
1 Job Difficult
Without 13 C
2 Control
Over Work 9 2
3 Job Performance 2 6 A
4 Addresses My
Needs 12 C
5 Saves Me Time 11 B
6 Work More
Quickly 7 3 B
7 Critical
to My
Job 5 4 C
8 Accomplish
More Work 6 7 B
9 Cut Unproductive
Time 10 B
10 Effectiveness 1 8 A
11 Quality
of Work 3 1 A
12 Increase Productivity 4 5 B
13 Makes Job Easier 8 9 C
14 Useful NA 10 NA
Table 4. Pretest Results: Perceived Ease of Use
Old New
Item # Item Rank Item # Cluster
1 Confusing 7 B
2 Error Prone 13
3 Frustrating 3 3 B
4 Dependence on Manual 9 (replace) C
5 Mental Effort 5 7 B
6 Error
Recovery 10
7 Rigid
& Inflexible 6 5 A
8 Controllable 1 4 A
9 Unexpected Behavior 11
10 Cumbersome 2 1 A
11 Understandable 4 8 B
12 Ease of Remembering 8 6 C
13 Provides
Guidance 12 (replace) C
14 Easy to Use NA 10 NA
NA Ease of Learning NA 2 NA
NA Effort
to Become Skillful NA 9 NA
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
Study 1
A field study was conducted to assess the reli-
ability,
convergent validity,
discriminant
validity,
and factorial
validity
of the 10-item scales re-
sulting
from
the pretest. A sample of 120 users
within IBM
Canada's Toronto
Development
Labo-
ratory
were given a questionnaire asking them
to rate the usefulness and ease of use of two
systems available there: PROFS electronic mail
and the XEDIT
file editor. The computing
envi-
ronment
consisted of IBM
mainframes accessi-
ble through
327X terminals. The PROFS elec-
tronic mail system is a simple but limited
messaging facility for brief messages. (See
Panko, 1988.) The XEDIT
editor
is widely
avail-
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
able on IBM
systems and offers both
full-screen
and command-driven
editing capabilities. The
questionnaire asked participants to rate the
extent to which
they agree with
each statement
by
circling
a number
from one to seven arranged
horizontally
beneath anchor point descriptions
"Strongly
Agree," "Neutral,"
and "Strongly
Dis-
agree." In
order
to ensure subject
familiarity
with
the systems being rated, instructions
asked the
participants
to skip over the section pertaining
to a given
system if
they never use it.
Responses
were obtained from 112 participants,
for a re-
sponse rate of 93%. Of these 112, 109 were
users of electronic mail and 75 were users of
XEDIT.
Subjects had an average of six months'
experience
with the two
systems studied.
Among
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
326 MIS
Quarterly/September
1989
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IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
IT
Usefulness
and
Ease
of Use
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
the sample, 10 percent
were managers,
35 per-
cent were administrative
staff, and 55 percent
were professional
staff (which
included
a broad
mix
of market
analysts,
product
development
ana-
lysts, programmers,
financial
analysts and re-
search scientists).
Reliability
and validity
The perceived usefulness scale attained
Cron-
bach alpha reliability
of .97 for both the elec-
tronic
mail and XEDIT
systems, while perceived
ease of use achieved a reliability
of .86 for elec-
tronic mail and .93 for XEDIT.
When observa-
tions were pooled for the two systems, alpha
was .97 for usefulness and .91 for ease of use.
Convergent
and discriminant
validity
were tested
using multitrait-multimethod
(MTMM)
analysis
(Campbell
and Fiske, 1959). The MTMM
matrix
contains the intercorrelations
of items (methods)
applied
to the two different
test systems (traits),
electronic
mail and XEDIT.
Convergent
validity
refers to whether
the items comprising
a scale
behave as if they are measuring
a common un-
derlying
construct.
In
order
to demonstrate
con-
vergent validity,
items that measure the same
trait should correlate highly with one another
(Campbell
and Fiske, 1959). That is, the ele-
ments in the monotrait
triangles (the submatrix
of intercorrelations
between items intended to
measure the same construct for the same
system) within
the MTMM
matrices should be
large.
For
perceived
usefulness,
the 90 monotrait-
heteromethod
correlations
were all significant
at
the .05 level. For ease of use, 86 out of 90,
or 95.6%, of the monotrait-heteromethod
corre-
lations were significant.
Thus, our
data supports
the convergent
validity
of the two scales.
Discriminant
validity
is concerned with
the abil-
ity of a measurement item to differentiate
be-
tween objects being measured. For instance,
within the MTMM
matrix,
a perceived
usefulness
item
applied
to electronic
mail
should not corre-
late too highly with the same item applied to
XEDIT. Failure
to discriminate
may suggest the
presence of "common
method
variance,"
which
means that an item
is measuring
methodological
artifacts
unrelated
to the target construct
(such
as individual
differences
in the style of respond-
ing
to questions (see Campbell,
et al., 1967; Silk,
1971) ). The test for discriminant
validity
is that
an item should correlate
more highly
with other
items intended to measure the same trait than
with either the same item used to measure a
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
different trait or
with different
items used to meas-
ure a different
trait
(Campbell
and Fiske, 1959).
For perceived usefulness, 1,800 such compari-
sons were confirmed
without
exception. Of the
1,800 comparisons for ease of use there were
58 exceptions (3%).
This represents an unusu-
ally high level of discriminant
validity
(Campbell
and Fiske, 1959; Silk, 1971) and implies that
the usefulness and ease of use scales possess
a high concentration
of trait
variance and are
not strongly influenced by methodological
artifacts.
Table 5 gives a summary
frequency
table of the
correlations
comprising
the MTMM
matrices
for
usefulness and ease of use. From
this table it
is possible to see the separation in magnitude
between monotrait
and heterotrait
correlations.
The frequency
table also shows that
the hetero-
trait-heteromethod
correlations
do not appear
to
be substantially
elevated above the heterotrait-
monomethod
correlations.
This is an additional
diagnostic suggested by Campbell and Fiske
(1959) to detect the presence of method
variance.
The few exceptions to the convergent and dis-
criminant
validity
that did
occur,
although
not
ex-
tensive enough to invalidate
the ease of use
scale, all involved negatively phrased ease of
use items.
These "reversed"
items
tended
to cor-
relate more with the same item used to meas-
ure a different
trait
than
they did
with
other
items
of the same trait,
suggesting the presence of
common method variance. This is ironic,
since
reversed scales are typically
used in an effort
to reduce common
method
variance.
Silk
(1971)
similarly
observed minor
departures
from con-
vergent and discriminant
validity
for reversed
items. The five positively
worded ease of use
items had a reliability
of .92 compared to .83
for the five negative items. This suggests an im-
provement
in the ease of use scale may be pos-
sible with the elimination
or reversal of nega-
tively phrased items. Nevertheless, the MTMM
analysis supported the ability of the 10-item
scales for
each construct
to differentiate
between
systems.
Factorial
validity
is concerned with
whether
the
usefulness and ease of use items form
distinct
constructs. A principal components analysis
using oblique rotation was performed
on the
twenty
usefulness and ease of use items. Data
were pooled across the two systems, for a total
of 184 observations.
The results show that the
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IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use IT
Usefulness and
Ease of Use
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
Table 5. Summary of Multitrait-Multimethod
Analyses
Construct
Perceived Usefulness Perceived Ease of Use
Same Trait/ Different Same Trait/ Different
Diff. Method Trait Diff. Method Trait
Correlation Elec. Same Diff. Elec. Same Diff.
Size Mail XEDIT Meth. Meth. Mail XEDIT Meth. Meth.
-.20 to -.11 1
-.10 to -.01 6 1 5
.00 to .09 3 25 2 1 32
.10 to .19 2 27 2 5 40
.20 to .29 5 25 9 1 11
.30 to .39 7 14 2 2 1
.40 to .49 9 9
.50 to .59 4 3 11
.60 to .69 14 4 3 13
.70 to .79 20 11 3 8
.80to .89 7 26 2
.90 to .99 4
# Correlations 45 45 10 90 45 45 10 90
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
usefulness and ease of use items load on dis-
tinct
factors
(Table 6). The multitrait-multimethod
analysis
and factor
analysis
both
support
the con-
struct
validity
of the 10-item
scales.
Scale refinement
In applied testing situations, it is important
to
keep scales as brief as possible, particularly
when multiple
systems are going to be evalu-
ated. The usefulness and ease of use scales
were refined and streamlined based on results
from Study 1 and then subjected to a second
round
of empirical
validation
in Study
2, reported
below. Applying
the Spearman-Brown
prophecy
formula
to the .97 reliability
obtained for per-
ceived usefulness indicates that a six-item
scale
composed of items having comparable
reliabil-
ity
would
yield a scale reliability
of .94. The five
positive ease of use items had a reliability
of
.92. Taken together, these findings
from Study
1 suggest that six items would be adequate to
achieve reliability
levels above .9 while main-
taining adequate validity
levels. Based on the
results of the field study, six of the 10 items for
each construct were selected to form modified
scales.
For the ease of use scale, the five negatively
worded items were eliminated due to their ap-
parent
common method
variance, leaving items
2, 4, 6, 8 and 10. Item 6 ("easy to remember
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
how to perform tasks"), which the pretest indi-
cated was concerned
with
ease of learning,
was
replaced by a reversal of item 9 ("easy to
become skillful"),
which was specifically de-
signed to more directly
tap ease of learning.
These items include two from cluster C, one
each from
clusters
A and B, and
the overall
item.
(See Table 4.) In order to improve representa-
tive coverage of the content domain, an addi-
tional A item was added. Of the two remaining
A items (#1, Cumbersome,
and #5, Rigid
and
Inflexible),
item 5 is readily
reversed to form "flex-
ible to interact with." This item was added to
form the sixth item, and the order of items 5
and 8 was permuted
in order to prevent items
from
the same cluster (items 4 and 5) from
ap-
pearing
next to one another.
In order to select six items to be used for the
usefulness scale, an item analysis was per-
formed. Corrected item-total
correlations were
computed for each item, separately for each
system studied. Average Z-scores of these cor-
relations
were used to rank
the items. Items 3,
5, 6, 8, 9 and 10 were top-ranked
items. Refer-
ring to the cluster analysis (Table 3), we see
that
this set is well-representative
of the content
domain,
including
two items from
cluster
A, two
from
cluster B and one from cluster C, as well
as the overall item (#10). The items were per-
muted to prevent items from the same cluster
from
appearing
next to one another. The result-
328 MIS
Quarterly/September
1989
328 MIS
Quarterly/September
1989
328 MIS
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1989
328 MIS
Quarterly/September
1989
328 MIS
Quarterly/September
1989
328 MIS
Quarterly/September
1989
328 MIS
Quarterly/September
1989
328 MIS
Quarterly/September
1989
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IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
Table 6. Factor Analysis of Perceived Usefulness and
Ease of Use Questions: Study 1
Factor 1 Factor 1
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Quality
of Work .80 .10
2 Control over Work .86 -.03
3 Work
More
Quickly .79 .17
4 Critical
to My
Job .87 -.11
5 Increase Productivity .87 .10
6 Job Performance .93 -.07
7 Accomplish
More
Work .91 -.02
8 Effectiveness .96 -.03
9 Makes Job Easier .80 .16
10 Useful .74 .23
Ease of Use
1 Cubersome .00 .73
2 Ease of Learning .08 .60
3 Frustrating .02 .65
4 Controllable .13 .74
5 Rigid
& Inflexible .09 .54
6 Ease of Remembering .17 .62
7 Mental
Effort -.07 .76
8 Understandable .29 .64
9 Effort to Be Skillful -.25 .88
10 Easy to Use .23 .72
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
ing six-item
usefulness and ease of use scales
are shown in the Appendix.
Relationship
to use
Participants were asked to self-report their
degree of current
usage of electronic mail and
XEDIT
on six-position categorical scales with
boxes labeled "Don't
use at all,"
"Use less than
once each week,"
"Use about
once each week,"
"Use several times a week," "Use about once
each day," and "Use several times each day."
Usage was significantly
correlated
with
both
per-
ceived usefulness and perceived ease of use
for both PROFS mail and XEDIT.
PROFS mail
usage correlated
.56 with perceived usefulness
and .32 with perceived ease of use. XEDIT
usage correlated .68 with usefulness and .48
with
ease of use. When
data
were pooled across
systems, usage correlated .63 with usefulness
and .45 with
ease of use. The overall
usefulness-
use correlation
was significantly
greater
than the
ease of use-use correlation
as indicated by a
test of dependent correlations (t181=3.69,
p<.001) (Cohen and Cohen, 1975). Usefulness
and ease of use were significantly
correlated
with
each other for electronic
mail (.56), XEDIT
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
(.69), and overall
(.64). All correlations were sig-
nificant at the .001 level.
Regression analyses were performed
to assess
the joint
effects of usefulness and ease of use
on usage. The effect of usefulness on usage,
controlling
for ease of use, was significant
at the
.001 level for electronic mail (b=.55), XEDIT
(b=.69), and pooled (b=.57). In contrast, the
effect of ease of use on usage, controlling
for
usefulness, was non-significant
across the board
(b=.01 for electronic mail; b=.02 for XEDIT;
and b=.07 pooled). In other words, the signifi-
cant pairwise
correlation
between ease of use
and usage vanishes when usefulness is con-
trolled
for. The regression coefficients
obtained
for each individual
system within each study
were not significantly different (F3, 178= 1.95,
n.s.). As the relationship
between independent
variables
in a regression
approach
perfect
linear
dependence, multicollinearity
can degrade the
parameter
estimates obtained.
Although
the cor-
relations between usefulness and ease of use
are significant, according to tests for multi-
collinearity
they are not large enough to com-
promise the accuracy of the estimated regres-
sion coefficients since the standard errors
of the
estimates are low (.08 for both usefulness and
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
MIS
Quarterly/September
1989 329
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IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use IT
Usefulness and Ease of Use
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
ease of use), and the covariances between the
parameter estimates are negligible (-.004)
(Johnston, 1972; Mansfield
and Helms, 1982).
Based on partial
correlation
analyses, the vari-
ance in usage explained by ease of use drops
by 98% when usefulness is controlled
for. The
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage, i.e., that ease of use influences
usage indirectly through
its effect on usefulness
(J.A. Davis, 1985).
Study 2
A lab study was performed
to evaluate the six-
item usefulness and ease of use scales result-
ing from scale refinement
in Study 1. Study 2
was designed to approximate
applied
prototype
testing or system selection situations,
an impor-
tant class of situations
where measures of this
kind
are likely
to be used in practice. In proto-
type testing and system selection contexts, pro-
spective users are typically
given a brief
hands-
on demonstration
involving
less than an hour
of
actually interacting
with the candidate system.
Thus, representative
users are asked to rate the
future
usefulness and ease of use they would
expect based on relatively
little
experience with
the systems being rated. We are especially in-
terested in the properties
of the usefulness and
ease of use scales when they are worded in
a prospective sense and are based on limited
experience with the target systems. Favorable
psychometric properties under these circum-
stances would be encouraging relative to their
use as early warning
indicants
of user accep-
tance (Ginzberg,
1981).
The lab study involved
40 voluntary
participants
who were evening MBA students at Boston Uni-
versity.
They were paid $25 for participating
in
the study. They had an average of five years'
work
experience and were employed
full-time in
several industries,
including
education (10 per-
cent),
government
(10 percent),
financial
(28 per-
cent), health
(18 percent),
and manufacturing (8
percent). They had a range of prior
experience
with computers in general (35 percent none or
limited;
48 percent moderate; and 17 percent
extensive) and personal computers
in particular
(35 percent none or limited;
48 percent moder-
ate; and 15 percent extensive) but were unfa-
miliar with
the two systems used in the study.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
The study involved evaluating two IBM PC-
based graphics systems: Chart-Master
(by De-
cision
Resources, Inc.
of Westport,
CN)
and Pen-
draw
(by Pencept, Inc.
of Waltham,
MA).
Chart-
Master is a menu-driven
package that creates
numerical business graphs, such as bar charts,
line charts,
and pie charts based on parameters
defined by the user. Through
the keyboard
and
menus, the user inputs
the data for,
and defines
the desired characteristics
of, the chart to be
made. The user can specify a wide variety
of
options
relating
to title
fonts, colors, plot
orienta-
tion, cross-hatching pattern,
chart format,
and
so on. The chart can then be previewed
on the
screen, saved, and printed.
Chart-Master
is a
successful commercial
product
that typifies
the
category
of numeric business charting programs.
Pendraw is quite different
from the typical
busi-
ness charting program.
It
uses bit-mapped graph-
ics and a "direct
manipulation"
interface where
users draw desired shapes using a digitizer
tablet and an electronic
"pen"
as a stylus. The
digitizer
tablet supplants the keyboard as the
input
medium.
By drawing
on a tablet, the user
manipulates
the image, which is visible on the
screen as it is being created. Pendraw offers
capabilities typical of PC-based, bit-mapped
"paint"
programs (see Panko, 1988), allowing
the user to perform
freehand
drawing
and select
from
among geometric
shapes, such as boxes,
lines, and circles. A variety
of line widths,
color
selections and title fonts are available. The
digitizer
is also capable of performing
character
recognition,
converting hand-printer
characters
into various fonts (Ward and Blesser, 1985).
Pencept had positioned
the Pendraw
product
to
complete with business charting
programs.
The
manual
introduces Pendraw
by guiding
the user
through
the process of creating a numeric bar
chart. Thus, a key marketing issue was the
extent to which the new product
would
compete
favorably
with
established
brands,
such as Chart-
Master.
Participants
were given one hour of hands-on
experience with Chart-Master
and Pendraw,
using workbooks that were designed to follow
the same instructional
sequence as the user
manuals for the two products,
while equalizing
the style of writing
and eliminating
value state-
ments (e.g., "See how easy that was to do?").
Half of the participants
tried Chart-Master
first
and half tried Pendraw first. After using each
package, a questionnaire
was completed.
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
330 MIS Quarterly/September 1989
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
Reliability
and validity
Cronbach
alpha was .98 for perceived useful-
ness and .94 for perceived ease of use. Con-
vergent validity
was supported,
with
only two of
72 monotrait-heteromethod correlations
falling
below significance. Ease of use item 4 (flexibil-
ity),
applied
to Chart-Master,
was not significantly
correlated
with either items 3 (clear and under-
standable) or 5 (easy to become skillful).
This
suggests that, contrary
to conventional
wisdom,
flexibility
is not always associated with ease of
use. As Goodwin
(1987) points
out,
flexibility
can
actually impair ease of use, particularly
for
novice users. With item 4 omitted, Cronbach
alpha for ease of use would increase from .94
to .95. Despite the two departures
to conver-
gent validity
related to ease of use item 4, no
exceptions
to the discriminant
validity
criteria oc-
curred across a total of 720 comparisons (360
for each scale).
Factorial
validity
was assessed by factor ana-
lyzing
the 12 scale items using principal compo-
nents extraction and oblique rotation. The re-
sulting
two-factor solution is very consistent with
distinct,
unidimensional usefulness and each of
use scales (Table 7). Thus, as in Study 1, Study
2 reflects
favorably
on the convergent,
discrimi-
nant,
and factorial
validity
of the usefulness and
ease of use scales.
Relationship
to use
Participants were asked to self-predict their
future use of Chart-Master
and Pendraw.
The
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
questions were worded as follows: "Assuming
Pendraw would be available on my job, I predict
that I
will
use it on a regular
basis in the future,"
followed by two seven-point scales, one with
likely-unlikely end-point
adjectives,
the other,
re-
versed in
polarity,
with
improbable-probable
end-
point adjectives. Such self-predictions,
or "be-
havioral
expectations,"
are among the most ac-
curate predictors available for an individual's
future behavior (Sheppard, et al., 1988; War-
shaw and Davis, 1985). For Chart-Master,
use-
fulness was significantly
correlated with self-
predicted
usage (r=.71, p<.001), but ease of
use was not (r=.25, n.s.) (Table 8). Chart-
Master had a non-significant
correlation
between
ease of use and usefulness (r=.25, n.s.). For
Pendraw,
usage was significantly
correlated with
both usefulness (r=.59, p<.001) and ease of
use (r=.47, p<.001). The ease of use-useful-
ness correlation was significiant
for Pendraw
(r=.38, p<.001). When
data were pooled
across
systems, usage correlated
.85 (p<.001) with
use-
fulness and .59 (p<.001) with ease of use (see
Table
8). Ease of use correlated
with
usefulness
.56 (p<.001). The overall usefulness-use corre-
lation
was significantly
greater
than the ease of
use-use correlation,
as indicated
by a test of de-
pendent
correlations
(t77
= 4.78, p<.001) (Cohen
and Cohen, 1975).
Regression analyses (Table
9) indicate that the
effect of usefulness on usage, controlling
for
ease of use, was significant
at the .001 level
for Chart-Master (b = .69), Pendraw (b = .76) and
overall (b=.75). In contrast,
the effect of ease
of use on usage, controlling
for usefulness, was
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
Table 7. Factor Analysis of Perceived Usefulness
and Ease of Use Items: Study 2
Factor 1 Factor 2
Scale Items (Usefulness) (Ease of Use)
Usefulness
1 Work
More
Quickly .91 .01
2 Job Performance .98 -.03
3 Increase Productivity .98 -.03
4 Effectiveness .94 .04
5 Makes Job Easier .95 -.01
6 Useful .88 .11
Ease of Use
1 Easy to Learn -.20 .97
2 Controllable .19 .83
3 Clear
& Understandable -.04 .89
4 Flexible .13 .63
5 Easy to Become Skillful .07 .91
6 Easy to Use .09 .91
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
MIS Quarterly/September 1989 331
This content downloaded from 130.184.237.6 on Thu, 6 Feb 2014 14:35:32 PM
All use subject to JSTOR Terms and Conditions

IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
Table 8. Correlations Between Perceived Usefulness,
Perceived Ease of Use, and Self-Reported
System Usage
Correlation
Usefulness Ease of Use Ease of Use
& Usage & Usage & Usefulness
Study 1
Electronic
Mail
(n- 109) .56*** .32*** .56***
XEDIT
(n=75) .68*** .48*** .69***
Pooled (n
=184) .63*** .45*** .64***
Study 2
Chart-Master
(n
= 40) .71*** .25 .25
Pendraw
(n
= 40) .59*** .47*** .38**
Pooled (n
= 80) .85*** .59*** .56***
Davis, et al. (1989) (n= 107)
Wave 1 .65*** .27** .10
Wave 2 .70*** .12 .23**
*** p<.001 ** p<.01 * p<.05
Table 9. Regression Analyses of the Effect of Perceived
Usefulness and Perceived Ease of Use on
Self-Reported Usage
Independent Variables
Usefulness Ease of Use R2
Study 1
Electronic
Mail
(n
= 109) .55*** .01 .31
XEDIT
(n
= 75) .69*** .02 .46
Pooled (n
=184) .57*** .07 .38
Study 2
Chart-Master
(n
= 40) .69*** .08 .51
Pendraw
(n=
40) .76*** .17 .71
Pooled (n
= 80) .75*** .17* .74
Davis, et al. (1989) (n= 107)
After 1 Hour .62*** .20*** .45
After 14 Weeks .71** -.06 .49
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01
*** p<.001 ** p<.01 * p<.05
* p<.05
* p<.05
* p<.05
* p<.05
* p<.05
* p<.05
* p<.05
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
non-significant
for both Chart-Master
(b=.08,
n.s.) and Pendraw
(b=.17, n.s.) when analyzed
separately and borderline
significant
when ob-
servations
were pooled (b= .17, p<.05). The re-
gression coefficients obtained for Pendraw and
Chart-Master
were not significantly
different
(F3,
74 = .014, n.s.). Multicollinearity
is ruled out since
the standard errors
of the estimates are low (.07
for both usefulness and ease of use) and the
covariances between the parameter
estimates
are negligible (-.004).
Hence, as in Study 1, the significant pairwise
correlations between ease of use and usage
drop
dramatically
when usefulness is controlled
for, suggesting that ease of use operates
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
through
usefulness. Partial
correlation
analysis
indicates that the variance in usage explained
by ease of use drops by 91% when usefulness
is controlled
for. Consistent with
Study 1, these
regression
and partial
correlation results
suggest
that usefulness mediates the effect of ease of
use on usage. The implications
of this are ad-
dressed in the following
discussion.
Discussion
The purpose
of this investigation
was to develop
and validate new measurement
scales for per-
ceived usefulness and perceived ease of use,
two distinct
variables hypothesized
to be deter-
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
332 MIS Quarterly/September 1989
This content downloaded from 130.184.237.6 on Thu, 6 Feb 2014 14:35:32 PM
All use subject to JSTOR Terms and Conditions

IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
IT
Usefulness
and
Ease of Use
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
minants of computer usage. This effort was suc-
cessful in
several respects. The new scales were
found to have strong psychometric properties
and to exhibit
significant
empirical
relationships
with
self-reported
measures of usage behavior.
Also, several new insights
were generated
about
the nature of perceived usefulness and ease of
use, and their roles as determinants of user
acceptance.
The new scales were developed, refined, and
streamlined in a several-step process. Explicit
definitions were stated, followed
by a theoretical
analysis from a variety
of perspectives, includ-
ing:
expectancy theory; self-efficacy theory;
be-
havioral
decision
theory;
diffusion of innovations;
marketing;
and human-computer
interaction,
re-
garding why usefulness and ease of use are hy-
pothesized as important
determinants of system
use. Based on the stated definitions,
initial
scale
items were generated. To enhance content va-
lidity,
these were pretested
in a small
pilot study,
and several items were eliminated. The remain-
ing items, 10 for each of the two constructs,
were
tested for validity
and reliability
in Study 1, a
field study of 112 users and two systems (the
PROFS electronic mail system and the XEDIT
file
editor).
Item
analysis was performed
to elimi-
nate more
items and refine
others,
further stream-
lining
and purifying
the scales. The resulting
six-
item scales were subjected to further
construct
validation
in Study 2, a lab study of 40 users
and two systems: Chart-Master
(a menu-driven
business charting
program)
and Pendraw
(a bit-
mapped paint
program
with
a digitizer
tablet as
its input
device).
The new scales exhibited excellent psychomet-
ric
characteristics.
Convergent
and discriminant
validity
were strongly supported by multitrait-
multimethod
analyses in both validation
studies.
These two data sets also provided strong sup-
port
for
factorial
validity:
the pattern
of factor
load-
ings confirmed
that a priori
structure
of the two
instruments,
with
usefulness items
loading highly
on one factor,
ease of use items loading highly
on the other
factor,
and small cross-factor
load-
ings. Cronbach
alpha reliability
for
perceived
use-
fulness was .97 in Study 1 and .98 in Study 2.
Reliability
for ease of use was .91 in Study 1
and .94 in Study
2. These findings
mutually
con-
firm
the psychometric
strength
of the new meas-
urement
scales.
As theorized, both perceived usefulness and
ease of use were significantly
correlated with
self-
reported
indicants
of system use. Perceived
use-
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
fulness was correlated .63 with
self-reported
cur-
rent use in Study 1 and .85 with self-predicted
use in Study
2. Perceived ease of use was cor-
related
.45 with
use in Study 1 and .69 in Study
2. The same pattern of correlations is found
when correlations
are calculated separately for
each of the two systems in each study (Table
8). These correlations,
especially
the usefulness-
use link,
compare favorably
with other correla-
tions between subjective measures and self-
reported
use found in the MIS
literature. Swan-
son's (1987) "value"
dimension correlated .20
with
use, while his "accessibility"
dimension cor-
related .13 with self-reported
use. Correlations
between "user
information
satisfaction"
and self-
reported
use of .39 (Barki
and Huff,
1985) and
.28 (Baroudi,
et al., 1986) have been reported.
"Realism of expectations"
has been found to be
correlated .22 with objectively measured use
(Ginzberg, 1981) and .43 with
self-reported
use
(Barki
and Huff,
1985). "Motiviational
force"
was
correlated
.25 with
system use, objectively
meas-
ured (DeSanctis, 1983). Among
the usage cor-
relations
reported
in the literature,
the .79 corre-
lation
between "performance"
and use reported
by Robey (1979) stands out. Recall that Robey's
expectancy model was a key underpinning
for
the definition
of perceived usefulness stated in
this article.
One of the most significant
findings
is the rela-
tive strength of the usefulness-usage relation-
ship compared to the ease of use-usage rela-
tionship. In both studies, usefulness was
significantly
more strongly
linked
to usage than
was ease of use. Examining
the joint
direct
effect
of the two variables
on use in regression analy-
ses, this difference
was even more pronounced:
the usefulness-usage relationship remained
large, while the ease of use-usage relationship
was diminished
substantially
(Table 8). Multi-
collinearity
has been ruled out as an explana-
tion for the results using specific tests for the
presence of multicollinearity.
In hindsight,
the
prominence of perceived usefulness makes
sense conceptually:
users are driven to adopt
an application
primarily
because of the functions
it performs
for them, and secondarily for how
easy or hard it is to get the system to perform
those functions. For instance, users are often
willing
to cope with some difficulty
of use in a
system that provides
critically
needed function-
ality. Although difficulty
of use can discourage
adoption of an otherwise useful system, no
amount of ease of use can compensate for a
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
MIS
Quarterly/September
1989 333
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
system that does not perform
a useful function.
The prominence
of usefulness over ease of use
has important
implications
for
designers, particu-
larly
in the human factors tradition,
who have
tended to overemphasize
ease of use and over-
look usefulness (e.g., Branscomb and Thomas,
1984; Chin, et al., 1988; Shneiderman,
1987).
Thus, a major
conclusion of this study is that
perceived usefulness is a strong correlate of
user acceptance and should not be ignored
by
those attempting
to design or implement
suc-
cessful systems.
From a causal perspective, the regression re-
sults suggest that ease of use may be an ante-
cedent to usefulness, rather than a parallel,
direct determinant of usage. The significant
pairwise correlation
between ease of use and
usage all but vanishes when usefulness is con-
trolled for. This, coupled with a significant
ease
of use-usefulness correlation
is exactly the pat-
tern one would expect if usefulness mediated
between ease of use and usage (e.g., J.A.
Davis, 1985). That is, the results are consistent
with an ease of use --> usefulness --> usage
chain of causality. These results held both for
pooled observations and for each individual
system (Table 8). The causal influence of ease
of use on usefulness makes sense conceptu-
ally, too. All else being equal, the easier a
system is to interact
with,
the less effort
needed
to operate it, and the more effort
one can allo-
cate to other activities
(Radner
and Rothschild,
1975), contributing
to overall job performance.
Goodwin
(1987) also argues for this flow of cau-
sality,
concluding
from her analysis that:
"There
is increasing evidence that the effective func-
tionality
of a system depends on its usability"
(p. 229). This intriguing interpretation
is prelimi-
nary
and should be subjected to further
experi-
mentation.
If true, however, it underscores the
theoretical
importance
of perceived usefulness.
This investigation
has limitations that should be
pointed out. The generality of the findings re-
mains to be shown by future research. The fact
that
similar
findings
were observed, with
respect
to both
the psychometric properties
of the meas-
ures and the pattern
of empirical
associations,
across two different user populations,
two differ-
ent systems, and two different research settings
(lab
and field),
provides
some evidence favoring
external
validity.
In addition,
a follow-up
to this study, reported
by Davis, et al. (1989) found a very similar
pat-
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
tern of results in a two-wave study (Tables 8
and 9). In
that
study, MBA
student
subjects
were
asked to fill
out a questionnaire
after a one-hour
introduction
to a word
processing program,
and
again 14 weeks later. Usage intentions were
measured at both time periods, and self-
reported
usage was measured at the later
time
period. Intentions
were significantly
correlated
with usage (.35 and .63 for the two points in
time, respectively).
Unlike
the results of Studies
1 and 2, Davis, et al. (1989) found a significant
direct
effect of ease of use on usage, controlling
for usefulness, after the one-hour
training
ses-
sion (Table
9), although
this evolved into a non-
significant
effect as of 14 weeks later. In gen-
eral, though, Davis, et al. (1989) found useful-
ness to be more influential
than ease of use in
driving
usage behavior,
consistent with
the find-
ings reported
above.
Further
research
will
shed more
light
on the gen-
erality
of these findings.
Another
limitation
is that
the usage measures employed were self-
reported
as opposed to objectively
measured.
Not enough is currently
known
about how accu-
rately
self-reports
reflect actual behavior.
Also,
since usage was reported
on the same ques-
tionnaire
used to measure usefulness and ease
of use, the possibility
of a halo effect should not
be overlooked.
Future
research addressing the
relationship
between these constructs and ob-
jectively
measured use is needed before claims
about the behavioral predictiveness can be
made
conclusively.
These limitations
notwithstand-
ing, the results represent a promising step
toward
the establishment of improved
measures
for two important
variables.
Research
implications
Future
research is needed to address how other
variables relate to usefulness, ease of use, and
acceptance. Intrinsic
motivation,
for example,
has received inadequate
attention in MIS
theo-
ries. Whereas perceived usefulness is con-
cerned
with
performance
as a consequence use,
intrinsic
motivation
is concerned with the rein-
forcement
and enjoyment
related
to the process
of performing
a behavior
per se, irrespective
of
whatever external outcomes are generated by
such behavior (Deci, 1975). Although
intrinsic
motivation
has been studied
in the design
of com-
puter
games (e.g., Malone, 1981), it is just be-
ginning
to be recognized as a potential
mecha-
nism underlying
user acceptance of end-user
334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989 334 MIS Quarterly/September 1989
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
systems (Carroll
and Thomas, 1988). Currently,
the role of affective attitudes is also an open
issue. While some theorists argue that beliefs
influence behavior only via their indirect influ-
ence on attitudes (e.g., Fishbein and Ajzen,
1975), others view beliefs and attitudes
as co-
determinants
of behavioral intentions
(e.g., Tri-
andis, 1977), and still others view attitudes as
antecedents of beliefs (e.g., Weiner, 1986).
Counter to Fishbein and Ajzen's
(1975) position,
both
Davis (1986) and Davis, et al. (1989) found
that attitudes do not fully
mediate the effect of
perceived
usefulness and perceived
ease of use
on behavior.
It
should be emphasized that perceived useful-
ness and ease of use are people's subjective
appraisal
of performance
and effort,
respectively,
and do not necessarily reflect
objective reality.
In
this
study,
beliefs are seen as meaningful
vari-
ables in their own right,
which function as be-
havioral
determinants,
and are not regarded
as
surrogate
measures of objective phenomena
(as
is often done in MIS
research, e.g., Ives, et al.,
1983; Srinivasan, 1985). Several MIS studies
have observed
discrepancies
between perceived
and actual performance
(Cats-Baril
and Huber,
1987; Dickson, et al., 1986; Gallupe and De-
Sanctis, 1988; Mcintyre,
1982; Sharda, et al.,
1988). Thus, even if an application
would
objec-
tively improve performance,
if users don't per-
ceive it as useful, they're
unlikely
to use it (Alavi
and Henderson,
1981). Conversely, people may
overrate the performance gains a system has
to offer and adopt systems that are dysfunc-
tional.
Given
that
this study indicates that
people
act according
to their
beliefs about
performance,
future
research is needed to understand
why
per-
formance beliefs are often in disagreement
with
objective
reality.
The possibility
of dysfunctional
impacts generated by information
technology
(e.g., Kottemann and Remus, 1987) emphasizes
that user acceptance is not a universal
goal and
is actually
undesireable
in cases where systems
fail to provide
true performance
gains.
More research is needed to understand how
measures such as those introduced
here per-
form in applied design and evaluation
settings.
The growing
literature
on design principles
(An-
derson and Olson, 1985; Gould and Lewis,
1985; Johansen and Baker, 1984; Mantei and
Teorey, 1988; Shneiderman,
1987) calls for the
use of subjective measures at various points
throughout
the development
and implementation
process, from the earliest needs assessment
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
through
concept screening and prototype
test-
ing
to post-implementation
assessment. The fact
that the measures performed
well psychometri-
cally both after brief introductions to the target
system (Study 2, and Davis, et al., 1989) and
after
substantial user experience
with the system
(Study 1, and Davis, et al., 1989) is promising
concerning their appropriateness at various
points in the life cycle. Practitioners
generally
evaluate systems not only to predict
acceptabil-
ity but also to diagnose the reasons underlying
lack of acceptance and to formulate interven-
tions to improve
user acceptance. In this sense,
research on how usefulness and ease of use
can be influenced
by various
externally
control-
lable factors, such as the functional and inter-
face characteristics of the system (Benbasat
and
Dexter, 1986; Bewley, et al., 1983; Dickson,
et
al., 1986), development methodologies (Alavi,
1984), training and education (Nelson and
Cheney, 1987), and user involvement in design
(Baroudi,
et al. 1986; Franz and Robey, 1986)
is important.
The new measures introduced
here
can be used by researchers investigating
these
issues.
Although
there has been a growing pessimism
in the field about the ability
to identify
measures
that are robustly
linked to user acceptance, the
view taken here is much more optimistic.
User
reactions
to computers
are complex and multi-
faceted. But if the field continues to systemati-
cally investigate
fundamental mechanisms driv-
ing user behavior,
cultivating
better and better
measures and
critically examining
alternative
theo-
retical models, sustainable progress is within
reach.
Acknowledgements
This research
was supported by grants
from the
MIT
Sloan School of Management,
IBM
Canada
Ltd.,
and The University
of Michigan
Business
School. The author is indebted to the anony-
mous associate editor and reviewers for their
many helpful
suggestions.
References
Abelson,
R.P.
and Levi,
A. "Decision
Making
and
Decision
Theory,"
in The Handbook of Social
Psychology, third
edition, G. Lindsay
and E.
Aronson
(eds.), Knopf,
New York, NY, 1985,
pp. 231-309.
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
MIS
Quarterly/September
1989 335
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IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use IT
Usefulness and
Ease
of Use
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Adelbratt,
T. and Montgomery,
H. "Attractiveness
of Decision Rules," Acta Psychologica (45),
1980, pp. 177-185.
Alavi, M. "An Analysis of the Prototyping
Ap-
proach
to Information
Systems Development,"
Communications of the ACM (27:6), June
1984, pp. 556-563.
Alavi,
M. and Henderson,
J.C. "An
Evolutionary
Strategy
for Implementing
a Decision Support
System," Management Science (27:11), No-
vember 1981, pp. 1309-1323.
Anastasi,
A. "Evolving
Concepts of Test Valida-
tion," Annual Review of Psychology (37),
1986, pp. 1-15.
Anderson,
N.S. and Olson, J.R. (eds.) Methods
for Designing Software to Fit Human
Needs
and Capabilities:
Proceedings of the Work-
shop on Software Human Factors, National
Academy Press, Washington,
D.C., 1985.
Bandura,
A. "Self-Efficacy
Mechanism
in Human
Agency,"
American
Psychologist (37:2), Feb-
ruary
1982, pp. 122-147.
Barki, H. and Huff, S. "Change, Attitude to
Change, and Decision Support
System Suc-
cess," Information
and Management
(9:5),
De-
cember 1985, pp. 261-268.
Baroudi, J.J., Olson, M.H. and Ives, B. "An Em-
pirical
Study of the Impact
of User Involve-
ment on System Usage and Information
Sat-
isfaction,"
Communications
of the ACM
(29:3),
March
1986, pp. 232-238.
Beach, L.R. and Mitchell,
T.R. "A
Contingency
Model for the Selection of Decision Strate-
gies,"
Academy of Management
Review (3:3),
July 1978, pp. 439-449.
Benbasat, I. and Dexter,
A.S. "An
Investigation
of the Effectiveness of Color and Graphical
Presentation
Under
Varying
Time
Constraints,
MIS
Quarterly
(10:1), March
1986, pp. 59-84.
Bewley, W.L.,
Roberts,
T.L.,
Schoit, D. and Ver-
plank, W.L., "Human
Factors Testing in the
Design of Xerox's
8010 'Star'
Office
Worksta-
tion,"
CHI '83 Human Factors in Computing
Systems, Boston, December 12-15, 1983,
ACM,
New York,
NY, pp. 72-77.
Bohrnstedt,
G.W.
"Reliability
and Validity
Assess-
ment in Attitude Measurement,"
in Attitude
Measurement, G.F. Summers (ed.), Rand-
McNally,
Chicago, IL,
1970, pp. 80-99.
Bowen, W. "The
Puny Payoff
from Office
Com-
puters,"
Fortune,
May 26, 1986, pp. 20-24.
Branscomb, L.M. and Thomas, J.C. "Ease of
Use: A System Design Challenge,"
IBM
Sys-
tems Journal
(23), 1984, pp. 224-235.
Campbell, D.T. and Fiske, D.W. "Convergent
and Discriminant
Validation
by the Multitrait-
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
Multitmethod
Matrix,"
Psychological Bulletin
(56:9), March
1959, pp. 81-105.
Campbell,
D.T., Siegman, C.R. and Rees, M.B.
"Direction-of-Wording
Effects in the Relation-
ships Between Scales," Psychological Bulle-
tin (68:5), November
1967, pp. 293-303.
Card,
S.K., Moran,
T.P. and Newell,
A. The
Psy-
chology of Human-Computer Interaction,
Erlbaum,
Hillsdale,
NJ, 1984.
Carroll,
J.M. and Carrithers,
C. "Training
Wheels
in a User Interface,"
Communications
of the
ACM
(27:8), August 1984, pp. 800-806.
Carroll, J.M. and McKendree, J. "Interface
Design Issues for Advice-Giving
Expert Sys-
tems," Communications
of the ACM (30:1,
January
1987, pp. 14-31.
Carroll, J.M.,
Mack, R.L.,
Lewis,
C.H.,
Grishkow-
sky, N.L. and Robertson,
S.R. "Exploring
Ex-
ploring
a Word
Processor,"
Human-Computer
Interaction
(1), 1985, pp. 283-307.
Carroll,
J.M. and Thomas, J.C. "Fun,"
SIGCHI
Bulletin
(19:3), January
1988, pp. 21-24.
Cats-Baril,
W.L.
and Huber,
G.P. "Decision
Sup-
port Systems for Ill-Structured
Problems:
An
Empirical
Study," Decision Sciences (18:3),
Summer 1987, pp. 352-372.
Cheney,
P.H., Mann,
R.I.
and Amoroso,
D.L. "Or-
ganizational
Factors
Affecting
the Success of
End-User Computing,"
Journal of Manage-
ment Information Systems (3:1), Summer
1986, pp. 65-80.
Chin, J.P., Diehl, V.A. and Norman,
K.L.
"De-
velopment of an Instrument
for Measuring
User Satisfaction
of the Human-Computer
In-
terface,"
CHI'88
Human Factors in Comput-
ing Systems, Washington,
D.C., May 15-19,
1988, ACM,
New York,
NY, pp. 213-218.
Cohen, J. and Cohen, P. Applied Multiple
Re-
gression/ Correlation
Analysis
for the Behav-
ioral Sciences, Erlbaum,
Hillsdale, NJ, 1975.
Curley,
K.F. "Are There any Real Benefits
from
Office Automation?"
Business Horizons (4),
July-August
1984, pp. 37-42.
Davis, F.D. "A Technology Acceptance Model
for Empirically
Testing New End-User Infor-
mation Systems: Theory and Results," doc-
toral
dissertation,
MIT Sloan School of Man-
agement, Cambridge,
MA,
1986.
Davis, F.D., Bagozzi, R.P. and Warshaw,
P.R.
User Acceptance of Computer
Technology:
A
Comparison
of Two
Theoretical
Models,"
Man-
agement Science (35:8), August 1989, pp.
982-1003.
Davis, J.A. The Logic of Causal Order,
Sage,
Beverly
Hills,
CA, 1985.
Deci, E.L. Intrinsic Motivation, Plenum, New
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
336 MIS
Quarterly/September
1989
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IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use IT
Usefulness
and Ease of Use
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
York,
NY, 1975.
DeSanctis, G. "Expectancy Theory
as an Expla-
nation of Voluntary
Use of a Decision Support
System," Psychological Reports (52), 1983,
pp. 247-260.
Dickson, G.W.,
DeSanctis, G. and McBride,
D.J.
"Understanding
the Effectiveness of Computer
Graphics
for Decision Support:
A Cumulative
Experimental
Approach,"
Communications of
the ACM (29:1), January 1986, pp. 40-47.
Edelmann, F. "Managers,
Computer
Systems,
and Productivity,"
MIS Quarterly
(5:3), Sep-
tember 1981, pp. 1-19.
Fishbein, M. and Ajzen, I. "Belief, Attitude,
In-
tention and Behavior: An Introduction to
Theory
and Research,"
Addison-Wesley,
Read-
ing, MA
1975.
Franz,
C.R. and Robey, D. "Organizational
Con-
text, User Involvement,
and the Usefulness of
Information
Systems," Decision Sciences
(17:3), Summer 1986, pp. 329-356.
Gallupe,
R.B., DeSanctis, G. and Dickson,
G.W.
"Computer-Based
Support
for
Group
Problem
Finding:
An Empirical Investigation,"
MIS
Quar-
terly (12:2), June 1988, pp. 277-296.
Ginzberg,
M.J.
"Early Diagnosis
of MIS
Implemen-
tation Failure:
Promising
Results and Unan-
swered Questions," Management Science
(27:4), April
1981, pp. 459-478.
Good, M.,
Spine, T.M., Whiteside,
J. and George
P. "User-Derived
Impact
Analysis as a Tool
for
Usability
Engineering,"
CHI'86 Human Fac-
tors in Computing Systems, Boston, April
13-
17, 1986, ACM,
New York,
New York
pp. 241-
246.
Goodwin,
N.C.
"Functionality
and Usability,"
Com-
munications of the ACM (30:3), March
1987,
pp. 229-233.
Goslar, M.D. "Capability
Criteria
for Marketing
Decision Support Systems," Journal of Man-
agement Information
Systems (3:1), Summer
1986, pp. 81-95.
Gould, J., Conti, J. and Hovanyecz, T. "Com-
posing
letters
with
a Simulated
Listening Type-
writer,"
Communications
of the ACM (26:4),
April
1983, pp. 295-308.
Gould,
J.D. and Lewis C. "Designing
for Usabil-
ity:
Key Principles
and What
Designers
Think,"
Communications of the ACM (28:3), March
1985, pp. 300-311.
Greenberg,
K.
"Executives Rate Their
PCs,"
PC
World,
September 1984, pp. 286-292.
Hauser,
J.R. and Simmie, P. "Profit
Maximizing
Perceptual
Positions:
An Integrated Theory
for
the Selection of Product
Features and Price,"
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
Management Science (27:1), January 1981,
pp. 33-56.
Hill,
T., Smith, N.D., and Mann, M.F. "Role of
Efficacy Expectations
in Predicting
the Deci-
sion to Use Advanced Technologies: The
Case of Computers,"
Journal
of Applied
Psy-
chology, (72:2), May 1987, pp. 307-313.
Ives, B.,
Olson,
M.H.
and
Baroudi,
J.J. "The
meas-
urement
of User Information
Satisfaction,"
Com-
munications of the ACM (26:10), October
1983, pp. 785-793.
Jarvenpaa, S.L. "The Effect of Task Demands
and Graphical
Format
on Information
Process-
ing Strategies," Management
Science (35:3),
March
1989, pp. 285-303.
Johansen, R. & Baker E., "User Needs Work-
shops: A New Approach
to Anticipating
User
Needs for Advanced Office Systems," Office
Technology and People (2), 1984, pp. 103-
119.
Johnson, E.J. and Payne, J.W. "Effort and Ac-
curacy in Choice," Management Science
(31:4), April
1985, pp. 395-414.
Johnston, J. Econometric Methods, McGraw-
Hill,
New York,
NY, 1972.
Klein,
G. and Beck, P.O. "A Decision Aid for
Selecting Among Information
Systems Alter-
natives,"
MIS
Quarterly
(11:2),
June 1987, pp.
177-186.
Kleinmuntz,
D.N. and Schkade, D.A. "The
Cog-
nitive Implications
of Information
Displays in
Computer-Supported
Decision-Making,"
Uni-
versity of Texas at Austin, Graduate
School
of Business,
Department
of Management
Work-
ing Paper 87/88-4-8, 1988.
Kottemann,
J.E. and Remus, W.E. "Evidence
and Principles
of Functional and Dysfunctional
DSS," OMEGA
(15:2), March
1987, pp. 135-
143.
Larcker,
D.F. and Lessig, V.P. "Perceived Use-
fulness of Information:
A Psychometric
Exami-
nation," Decision Sciences (11:1), January
1980, pp. 121-134.
Lucas, H.C. "Performance and the Use of an
Information
System," Management Science
(21:8), April
1975, pp. 908-919.
Malone,
T.W. "Toward a Theory
of Intrinsically
Motivating
Instruction,"
Cognitive
Science (4),
1981, pp. 333-369.
Mansfield,
E.R.
and Helms,
B.P. "Detecting
Mul-
ticollinearity,"
The
American
Statistician
(36:3),
August 1982, pp. 158-160.
Mantei, M.M.
and Teorey, T.J. "Cost/Benefit
Analysis for Incorporating
Human Factors in
the Software Lifecycle,"
Communications
of
the ACM
(31:4), April
1988, pp. 428-439.
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
MIS
Quarterly/September
1989 337
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IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
IT
Usefulness and Ease of Use
Markus,
M.L. and Bjorn-Anderson,
N. "Power
Over Users: It's Exercise by System Profes-
sionals,"
Communications
of the ACM
(30:6),
June 1987, pp. 498-504.
Mcintyre, S. "An Experimental Study of the
Impact of Judgement-Based Marketing
Models,"
Management Science (28:1), Janu-
ary 1982, pp. 17-23.
Nelson, R.R. and Cheney, P.H. "Training
End
Users: An Exploratory
Study,"
MIS Quarterly
(11:4), December 1987, pp. 547-559.
Nickerson,
R.S. "Why
Interactive
Computer Sys-
tems Are Sometimes Not Used by People
Who Might
Benefit from
Them,"
International
Journal of Man-Machine
Studies (15), 1981,
pp. 469-483.
Norman, D.A. "Design Principles for Human-
Computer
Interfaces,"
CHI '83 Human Fac-
tors in Computing Systems, Boston, Decem-
ber 12-15, 1983, ACM,
New York, NY, pp. 1-
10.
Nunnally,
J. Psychometric
Theory,
McGraw-Hill,
New York, NY, 1978.
Panko, R.R. End-User Computing: Manage-
ment, Applications, and Technology, Wiley,
New York, NY, 1988.
Payne, J. W. "Contingent
Decision Behavior,"
Psychological Bulletin,
(92:2), 1982, pp. 382-
402.
Pfeffer, J. Organizations and Organization
Theory,
Pitman, Boston, MA,
1982.
Radner,
R. and Rothschild,
M. "On
the Alloca-
tion of Effort,"
Journal of Economic Theory
(10), 1975, pp. 358-376.
Roberts,
T.L. and Moran,
T.P. "The Evaluation
of Text
Editors:
Methodology
and Empirical
Re-
sults," Communications
of the ACM (26:4),
April
1983, pp. 265-283.
Robey, D. "User
Attitudes and Management
In-
formation
System Use,"
Academy
of Manage-
ment
Journal
(22:3),
September
1979, pp. 527-
538.
Robey, D. and Farrow,
D. "User Involvement
in
Information
System Development:
A Conflict
Model and Empirical
Test,"
Management
Sci-
ence (28:1), January 1982, pp. 73-85.
Rogers, E.M. and Shoemaker, F.F. Communi-
cation of Innovations:
A Cross-Cultural
Ap-
proach, Free Press, New York, NY, 1971.
Rushinek,
A. and Rushinek,
S.F. "What
Makes
Users Happy?"
Communications
of the ACM
(29:7), July 1986, pp. 594-598.
Saracevic, T. "Relevance:
A Review of and a
Framework for the Thinking
on the Notion
in
Information
Science," Journal of the Ameri-
can Society for Information
Science, Novem-
Markus,
M.L. and Bjorn-Anderson,
N. "Power
Over Users: It's Exercise by System Profes-
sionals,"
Communications
of the ACM
(30:6),
June 1987, pp. 498-504.
Mcintyre, S. "An Experimental Study of the
Impact of Judgement-Based Marketing
Models,"
Management Science (28:1), Janu-
ary 1982, pp. 17-23.
Nelson, R.R. and Cheney, P.H. "Training
End
Users: An Exploratory
Study,"
MIS Quarterly
(11:4), December 1987, pp. 547-559.
Nickerson,
R.S. "Why
Interactive
Computer Sys-
tems Are Sometimes Not Used by People
Who Might
Benefit from
Them,"
International
Journal of Man-Machine
Studies (15), 1981,
pp. 469-483.
Norman, D.A. "Design Principles for Human-
Computer
Interfaces,"
CHI '83 Human Fac-
tors in Computing Systems, Boston, Decem-
ber 12-15, 1983, ACM,
New York, NY, pp. 1-
10.
Nunnally,
J. Psychometric
Theory,
McGraw-Hill,
New York, NY, 1978.
Panko, R.R. End-User Computing: Manage-
ment, Applications, and Technology, Wiley,
New York, NY, 1988.
Payne, J. W. "Contingent
Decision Behavior,"
Psychological Bulletin,
(92:2), 1982, pp. 382-
402.
Pfeffer, J. Organizations and Organization
Theory,
Pitman, Boston, MA,
1982.
Radner,
R. and Rothschild,
M. "On
the Alloca-
tion of Effort,"
Journal of Economic Theory
(10), 1975, pp. 358-376.
Roberts,
T.L. and Moran,
T.P. "The Evaluation
of Text
Editors:
Methodology
and Empirical
Re-
sults," Communications
of the ACM (26:4),
April
1983, pp. 265-283.
Robey, D. "User
Attitudes and Management
In-
formation
System Use,"
Academy
of Manage-
ment
Journal
(22:3),
September
1979, pp. 527-
538.
Robey, D. and Farrow,
D. "User Involvement
in
Information
System Development:
A Conflict
Model and Empirical
Test,"
Management
Sci-
ence (28:1), January 1982, pp. 73-85.
Rogers, E.M. and Shoemaker, F.F. Communi-
cation of Innovations:
A Cross-Cultural
Ap-
proach, Free Press, New York, NY, 1971.
Rushinek,
A. and Rushinek,
S.F. "What
Makes
Users Happy?"
Communications
of the ACM
(29:7), July 1986, pp. 594-598.
Saracevic, T. "Relevance:
A Review of and a
Framework for the Thinking
on the Notion
in
Information
Science," Journal of the Ameri-
can Society for Information
Science, Novem-
Markus,
M.L. and Bjorn-Anderson,
N. "Power
Over Users: It's Exercise by System Profes-
sionals,"
Communications
of the ACM
(30:6),
June 1987, pp. 498-504.
Mcintyre, S. "An Experimental Study of the
Impact of Judgement-Based Marketing
Models,"
Management Science (28:1), Janu-
ary 1982, pp. 17-23.
Nelson, R.R. and Cheney, P.H. "Training
End
Users: An Exploratory
Study,"
MIS Quarterly
(11:4), December 1987, pp. 547-559.
Nickerson,
R.S. "Why
Interactive
Computer Sys-
tems Are Sometimes Not Used by People
Who Might
Benefit from
Them,"
International
Journal of Man-Machine
Studies (15), 1981,
pp. 469-483.
Norman, D.A. "Design Principles for Human-
Computer
Interfaces,"
CHI '83 Human Fac-
tors in Computing Systems, Boston, Decem-
ber 12-15, 1983, ACM,
New York, NY, pp. 1-
10.
Nunnally,
J. Psychometric
Theory,
McGraw-Hill,
New York, NY, 1978.
Panko, R.R. End-User Computing: Manage-
ment, Applications, and Technology, Wiley,
New York, NY, 1988.
Payne, J. W. "Contingent
Decision Behavior,"
Psychological Bulletin,
(92:2), 1982, pp. 382-
402.
Pfeffer, J. Organizations and Organization
Theory,
Pitman, Boston, MA,
1982.
Radner,
R. and Rothschild,
M. "On
the Alloca-
tion of Effort,"
Journal of Economic Theory
(10), 1975, pp. 358-376.
Roberts,
T.L. and Moran,
T.P. "The Evaluation
of Text
Editors:
Methodology
and Empirical
Re-
sults," Communications
of the ACM (26:4),
April
1983, pp. 265-283.
Robey, D. "User
Attitudes and Management
In-
formation
System Use,"
Academy
of Manage-
ment
Journal
(22:3),
September
1979, pp. 527-
538.
Robey, D. and Farrow,
D. "User Involvement
in
Information
System Development:
A Conflict
Model and Empirical
Test,"
Management
Sci-
ence (28:1), January 1982, pp. 73-85.
Rogers, E.M. and Shoemaker, F.F. Communi-
cation of Innovations:
A Cross-Cultural
Ap-
proach, Free Press, New York, NY, 1971.
Rushinek,
A. and Rushinek,
S.F. "What
Makes
Users Happy?"
Communications
of the ACM
(29:7), July 1986, pp. 594-598.
Saracevic, T. "Relevance:
A Review of and a
Framework for the Thinking
on the Notion
in
Information
Science," Journal of the Ameri-
can Society for Information
Science, Novem-
Markus,
M.L. and Bjorn-Anderson,
N. "Power
Over Users: It's Exercise by System Profes-
sionals,"
Communications
of the ACM
(30:6),
June 1987, pp. 498-504.
Mcintyre, S. "An Experimental Study of the
Impact of Judgement-Based Marketing
Models,"
Management Science (28:1), Janu-
ary 1982, pp. 17-23.
Nelson, R.R. and Cheney, P.H. "Training
End
Users: An Exploratory
Study,"
MIS Quarterly
(11:4), December 1987, pp. 547-559.
Nickerson,
R.S. "Why
Interactive
Computer Sys-
tems Are Sometimes Not Used by People
Who Might
Benefit from
Them,"
International
Journal of Man-Machine
Studies (15), 1981,
pp. 469-483.
Norman, D.A. "Design Principles for Human-
Computer
Interfaces,"
CHI '83 Human Fac-
tors in Computing Systems, Boston, Decem-
ber 12-15, 1983, ACM,
New York, NY, pp. 1-
10.
Nunnally,
J. Psychometric
Theory,
McGraw-Hill,
New York, NY, 1978.
Panko, R.R. End-User Computing: Manage-
ment, Applications, and Technology, Wiley,
New York, NY, 1988.